1  Introduction

In this work, a tool called control-oriented Cluster-based Network Modeling (CNMc) is further developed. The overall goal, in very brief terms, is to generate a model, which is able to predict the trajectories of general dynamical systems. The model shall be capable of predicting the trajectories when a model parameter value is changed. Some basics about dynamical systems are covered in subsection 3.0.1 and in-depth explanations about CNMccontrol-oriented Cluster-based Network Modeling are given in chapter 5.

However, for a short and broad introduction to CNMccontrol-oriented Cluster-based Network Modeling the workflow depicted in figure 1.1 shall be highlighted. The input it receives is data of a dynamical system or space state vectors for a range of model parameter values. The two main important outcomes are some accuracy measurements and the predicted trajectory for each desired model parameter value. Any inexperienced user may only have a look at the predicted trajectories to quickly decide visually whether the prediction matches the trained data. Since CNMccontrol-oriented Cluster-based Network Modeling is written in a modular manner, meaning it can be regarded as a black-box function, it can easily be integrated into other existing codes or workflows.

Figure 1.1— Broad overview: Workflow of